# French Real Estate Deal Score (`tagadanar/french-real-estate-deal-score`) Actor
Score French owner-direct property listings (PAP) against official sold prices (DVF): each listing gets an over/under-market deal score from the area's real €/m². Spot underpriced sales fast. Agent-ready via MCP.
- **URL**: https://apify.com/tagadanar/french-real-estate-deal-score.md
- **Developed by:** [Raphaël R](https://apify.com/tagadanar) (community)
- **Categories:** Real estate, Lead generation, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
from $6.00 / 1,000 listing scoreds
This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".
## How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
## French Real Estate Deal Score — Asking vs Sold Price (PAP + DVF)
**Find underpriced French properties in seconds.** This actor takes a [PAP.fr](https://www.pap.fr) owner-direct sale search and scores every listing against **what comparable properties actually sold for**, using the French government's official transaction database (**DVF** — *Demandes de Valeurs Foncières*).
Each listing gets a **deal score**: how far its asking €/m² sits **below (good) or above (expensive)** the area's real, recorded median. No more guessing whether a price is fair — you see it.
Built for **property investors, buyers, agents doing pige, and AI agents** (API + MCP).
### Why this actor
- ✅ **Asking vs achieved, automatically** — live owner asking prices (PAP) benchmarked against official sold prices (DVF), per property type, in the same commune.
- ✅ **A single deal score per listing** — e.g. `+18` means the asking €/m² is 18 % below the local median sale; `-12` means 12 % above. Sorted best-deal-first.
- ✅ **Owner-direct** — PAP has no agencies, so a good deal means a direct-to-owner opportunity (add `scrapeDetails` for the owner's phone).
- ✅ **Official, free benchmark data** — DVF from files.data.gouv.fr (Etalab / DGFiP). Outliers (symbolic €1 sales, garages) filtered out.
- ✅ **Paste one URL** — the benchmark area is auto-derived; override it only if you want.
### How it works
1. Scrapes the PAP sale listings from your search URL (asking prices).
2. Pulls DVF sold transactions for the same commune and years.
3. Builds a median €/m² benchmark per property type (Apartment / House), ignoring data-entry noise.
4. Scores each listing: `dealScore = (median_sold − asking) / median_sold × 100`.
5. Returns listings **best-deal-first**, each carrying the benchmark it was judged against.
### Input
```json
{
"searchUrl": "https://www.pap.fr/annonce/vente-appartements-aix-en-provence-13-g11424",
"dvfLocation": "",
"dvfYears": ["2024", "2025"],
"scrapeDetails": false,
"maxListings": 200
}
````
- `searchUrl` — a pap.fr **sale** search URL (asking prices to score).
- `dvfLocation` — optional commune/postal/INSEE for the benchmark; empty = auto-derive.
- `dvfYears` — DVF years to build the benchmark from (more = larger sample).
- `scrapeDetails` — also fetch each owner's phone and street address.
### Output (one record per listing)
```json
{
"source": "PAP",
"propertyType": "Appartement",
"price": 249000,
"surface": 55,
"pricePerM2": 4527,
"dealScore": 19,
"dealLabel": "Great deal (well below market)",
"benchmarkPerM2": 5600,
"benchmarkSample": 214,
"benchmarkArea": "13100",
"benchmarkYears": ["2024", "2025"],
"location": "Aix-En-Provence",
"url": "https://www.pap.fr/annonces/appartement-…-r462001771",
"phone": null
}
```
`dealScore` is `null` for rentals, listings without a surface, or areas with too few comparable sales (in which case `dealLabel` explains why). The run's `BENCHMARK` key-value record holds the full per-type benchmark and the communes used.
### Pricing
Pay per event — no subscription:
| Event | When |
|---|---|
| Actor start | Once per run |
| Listing scored | Per listing returned (scored against DVF) |
### Companion actors
- [**PAP Real Estate Listings**](https://apify.com/tagadanar/french-real-estate-pap) — raw owner-direct listings (this actor's asking-price source).
- [**French Real Estate Sales (DVF)**](https://apify.com/tagadanar/french-real-estate-dvf) — the raw sold-price transactions (this actor's benchmark source).
### FAQ
**Where do the numbers come from?** Asking prices: public listings on pap.fr. Sold prices: DVF open data (files.data.gouv.fr/geo-dvf, Etalab / DGFiP). Both fetched live at run time.
**How is a "deal" defined?** Purely on €/m² vs the local median recorded sale for the same property type. It's a pricing signal, not an appraisal — always verify condition, floor, exact street, and DPE.
**Why is a score null?** Rentals (no sale comparable), listings without a usable surface, or a benchmark with fewer than 5 comparable sales. The benchmark context is still returned so you can judge.
**Paris / Lyon / Marseille?** Handled — their arrondissements are expanded automatically for DVF.
**Does it work with AI agents?** Yes — REST API and **MCP**. Ask *"show me PAP apartments in Aix priced under the DVF median."*
***
*Keywords: immobilier, prix au m², DVF, valeurs foncières, PAP, particulier à particulier, sous-évalué, bonne affaire immobilière, deal score, asking vs sold price, real estate France, property investment, pige immobilière, €/m².*
# Actor input Schema
## `searchUrl` (type: `string`):
A pap.fr sale search URL — these owner-direct asking prices are scored against official sold prices. Build the search on pap.fr (city, apartment/house, filters) and paste its URL. Example: https://www.pap.fr/annonce/vente-appartements-aix-en-provence-13-g11424
## `dvfLocation` (type: `string`):
Commune name, postal code or INSEE code for the DVF sold-price benchmark. Leave empty to auto-derive it from the listings / search URL. Set it to widen or pin the comparison area (e.g. 13100 or Aix-en-Provence).
## `dvfYears` (type: `array`):
Which DVF years of sold prices to build the benchmark from. More years = larger sample. Available roughly 2021–2025.
## `scrapeDetails` (type: `boolean`):
Open each listing page to add the owner's phone and street address — handy for acting on the best deals. Slower (one page per listing).
## `minPrice` (type: `integer`):
Keep only listings at or above this price.
## `maxPrice` (type: `integer`):
Keep only listings at or below this price.
## `minSurface` (type: `integer`):
Keep only listings with at least this built surface.
## `minRooms` (type: `integer`):
Keep only listings with at least this many rooms.
## `propertyTypes` (type: `array`):
Keep only these types. Scoring benchmarks Apartments and Houses.
## `keywords` (type: `array`):
Keep only listings whose description/location contains one of these words.
## `maxListings` (type: `integer`):
Stop after scoring this many listings.
## `maxPagesPerUrl` (type: `integer`):
Safety cap on PAP pagination depth.
## Actor input object example
```json
{
"searchUrl": "https://www.pap.fr/annonce/vente-appartements-aix-en-provence-13-g11424",
"dvfYears": [
"2024",
"2025"
],
"scrapeDetails": false,
"propertyTypes": [],
"keywords": [],
"maxListings": 200,
"maxPagesPerUrl": 20
}
```
# Actor output Schema
## `listings` (type: `string`):
Owner-direct listings enriched with a DVF-based deal score (best deals first).
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"searchUrl": "https://www.pap.fr/annonce/vente-appartements-aix-en-provence-13-g11424",
"dvfLocation": "",
"dvfYears": [
"2024",
"2025"
]
};
// Run the Actor and wait for it to finish
const run = await client.actor("tagadanar/french-real-estate-deal-score").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
```
## Python example
```python
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {
"searchUrl": "https://www.pap.fr/annonce/vente-appartements-aix-en-provence-13-g11424",
"dvfLocation": "",
"dvfYears": [
"2024",
"2025",
],
}
# Run the Actor and wait for it to finish
run = client.actor("tagadanar/french-real-estate-deal-score").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"searchUrl": "https://www.pap.fr/annonce/vente-appartements-aix-en-provence-13-g11424",
"dvfLocation": "",
"dvfYears": [
"2024",
"2025"
]
}' |
apify call tagadanar/french-real-estate-deal-score --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=tagadanar/french-real-estate-deal-score",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "French Real Estate Deal Score",
"description": "Score French owner-direct property listings (PAP) against official sold prices (DVF): each listing gets an over/under-market deal score from the area's real €/m². Spot underpriced sales fast. Agent-ready via MCP.",
"version": "0.1",
"x-build-id": "b9OpiR6qPr3M0CNix"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/tagadanar~french-real-estate-deal-score/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-tagadanar-french-real-estate-deal-score",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/tagadanar~french-real-estate-deal-score/runs": {
"post": {
"operationId": "runs-sync-tagadanar-french-real-estate-deal-score",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/tagadanar~french-real-estate-deal-score/run-sync": {
"post": {
"operationId": "run-sync-tagadanar-french-real-estate-deal-score",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"required": [
"searchUrl"
],
"properties": {
"searchUrl": {
"title": "PAP search URL (sale listings)",
"type": "string",
"description": "A pap.fr sale search URL — these owner-direct asking prices are scored against official sold prices. Build the search on pap.fr (city, apartment/house, filters) and paste its URL. Example: https://www.pap.fr/annonce/vente-appartements-aix-en-provence-13-g11424"
},
"dvfLocation": {
"title": "Benchmark area (optional)",
"type": "string",
"description": "Commune name, postal code or INSEE code for the DVF sold-price benchmark. Leave empty to auto-derive it from the listings / search URL. Set it to widen or pin the comparison area (e.g. 13100 or Aix-en-Provence)."
},
"dvfYears": {
"title": "Benchmark years",
"type": "array",
"description": "Which DVF years of sold prices to build the benchmark from. More years = larger sample. Available roughly 2021–2025.",
"items": {
"type": "string"
}
},
"scrapeDetails": {
"title": "Also fetch owner phone & full address",
"type": "boolean",
"description": "Open each listing page to add the owner's phone and street address — handy for acting on the best deals. Slower (one page per listing).",
"default": false
},
"minPrice": {
"title": "Minimum price (€)",
"minimum": 0,
"type": "integer",
"description": "Keep only listings at or above this price."
},
"maxPrice": {
"title": "Maximum price (€)",
"minimum": 0,
"type": "integer",
"description": "Keep only listings at or below this price."
},
"minSurface": {
"title": "Minimum surface (m²)",
"minimum": 0,
"type": "integer",
"description": "Keep only listings with at least this built surface."
},
"minRooms": {
"title": "Minimum rooms",
"minimum": 0,
"type": "integer",
"description": "Keep only listings with at least this many rooms."
},
"propertyTypes": {
"title": "Property types",
"type": "array",
"description": "Keep only these types. Scoring benchmarks Apartments and Houses.",
"items": {
"type": "string",
"enum": [
"Appartement",
"Maison",
"Terrain",
"Immeuble",
"Local commercial",
"Parking / Box"
],
"enumTitles": [
"Apartment",
"House",
"Land",
"Building",
"Commercial",
"Parking / Box"
]
},
"default": []
},
"keywords": {
"title": "Keywords",
"type": "array",
"description": "Keep only listings whose description/location contains one of these words.",
"default": [],
"items": {
"type": "string"
}
},
"maxListings": {
"title": "Max listings",
"minimum": 1,
"maximum": 5000,
"type": "integer",
"description": "Stop after scoring this many listings.",
"default": 200
},
"maxPagesPerUrl": {
"title": "Max search pages",
"minimum": 1,
"maximum": 200,
"type": "integer",
"description": "Safety cap on PAP pagination depth.",
"default": 20
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```